ArticleJournal of digital imaging2016
Localization of Diagnostically Relevant Regions of Interest in Whole Slide Images: a Comparative Study.
Article in Journal of digital imaging, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
25 citing papers in PubMed.
- Robust ROI Detection in Whole Slide Images Guided by Pathologists' Viewing Patterns.Journal of imaging informatics in medicine · 2025Article
- NMGrad: Advancing Histopathological Bladder Cancer Grading with Weakly Supervised Deep Learning.Bioengineering (Basel, Switzerland) · 2024Article
- PathEX: Make good choice for whole slide image extraction.PloS one · 2024Article
- Multiplex imaging in immuno-oncology.Journal for immunotherapy of cancer · 2023Review
- Applications of discriminative and deep learning feature extraction methods for whole slide image analysis: A survey.Journal of pathology informatics · 2023Review
- Extracting decision-making features from the unstructured eye movements of clinicians on glaucoma OCT reports and developing AI models to classify expertise.Frontiers in medicine · 2023Article
- Improving the Diagnosis of Skin Biopsies Using Tissue Segmentation.Diagnostics (Basel, Switzerland) · 2022Article
- End-to-End diagnosis of breast biopsy images with transformers.Medical image analysis · 2022Article
- Chronic Lymphocytic Leukemia Progression Diagnosis with Intrinsic Cellular Patterns via Unsupervised Clustering.Cancers · 2022Article
- Multiplex Immunofluorescence and the Digital Image Analysis Workflow for Evaluation of the Tumor Immune Environment in Translational Research.Frontiers in oncology · 2022Review
- Assessment of the Effect of Cleanliness on the Visual Inspection of Aircraft Engine Blades: An Eye Tracking Study.Sensors (Basel, Switzerland) · 2021Article
- Analysis of Regions of Interest and Distractor Regions in Breast Biopsy Images.... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics · 2021Article
- Deep Feature Representations for Variable-Sized Regions of Interest in Breast Histopathology.IEEE journal of biomedical and health informatics · 2021Article
- MLCD: A Unified Software Package for Cancer Diagnosis.JCO clinical cancer informatics · 2020Article
- A deep learning image-based intrinsic molecular subtype classifier of breast tumors reveals tumor heterogeneity that may affect survival.Breast cancer research : BCR · 2020Article
- Localization of Nuclei in Breast Cancer Using Whole Slide Imaging System Supported by Morphological Features and Shape Formulas.Cancer management and research · 2020Article
- Assessment of Machine Learning of Breast Pathology Structures for Automated Differentiation of Breast Cancer and High-Risk Proliferative Lesions.JAMA network open · 2019Article
- A review of eye tracking for understanding and improving diagnostic interpretation.Cognitive research: principles and implications · 2019Article
- Detection and classification of cancer in whole slide breast histopathology images using deep convolutional networks.Pattern recognition · 2018Article
- Machine Learning Methods for Histopathological Image Analysis.Computational and structural biotechnology journal · 2018Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Whole slide digital imaging technology enables researchers to study pathologists' interpretive behavior as they view digital slides and gain new understanding of the diagnostic medical decision-making process. In this study, we propose a simple yet important analysis to extract diagnostically relevant regions of interest (ROIs) from tracking records using only pathologists' actions as they viewed biopsy specimens in the whole slide digital imaging format (zooming, panning, and fixating). We use these extracted regions in a visual bag-of-words model based on color and texture features to predict diagnostically relevant ROIs on whole slide images. Using a logistic regression classifier in a cross-validation setting on 240 digital breast biopsy slides and viewport tracking logs of three expert pathologists, we produce probability maps that show 74 % overlap with the actual regions at which pathologists looked. We compare different bag-of-words models by changing dictionary size, visual word definition (patches vs. superpixels), and training data (automatically extracted ROIs vs. manually marked ROIs). This study is a first step in understanding the scanning behaviors of pathologists and the underlying reasons for diagnostic errors.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.